Vascular smooth muscle cell (VSMCs) cell diversification drives atherosclerotic coronary artery disease (CAD). Mechanisms governing these cell state transitions remain unclear. We applied multi-omic single-cell profiling, epitope mapping, and spatial transcriptomics across 27 human coronary arteries, identifying fibroblast activation protein (FAP) as a marker of modulated VSMCs. Lineage tracing in mice indicated that FAP⁺ cells originate from Myh11⁺ VSMCs, and FAP PET imaging in CAD patients showed plaque uptake. FAP⁺ cells states resided in the macrophage-rich neo-intima. Therapeutically, we developed an anti-FAP bispecific T-cell engager, which reduced plaque burden and remodeled the stromal–immune microenvironment through T-cell clonal expansion. Our study delivers a single-cell and spatial atlas of human CAD, establishes FAP as a marker of modulated VSMCs, and highlights immunotherapy for lipid-independent targets.
from sddb import Catalog
cohort = Catalog().collection("geo-gse314851")
cohort.members # the 16 datasets as a table
print(cohort.licenses()) # check the terms before you download
sdatas = cohort.to_dict() # {uid: SpatialData}, cached locally
# or keep the .zarr files in a folder of your choice:
# sdatas = cohort.to_dict(dest="data/")Needs pip install lamindb and network access to the Lamin database, which is limited during the alpha. If it fails, use the Client tab.
import lamindb as ln
db = ln.DB("scverse/spatialdata-db")
coll = db.Collection.get(key="geo-gse314851")
coll.ordered_artifacts.to_dataframe() # the 16 members, ordered
# download every member .zarr to the local cache:
paths = [a.cache() for a in coll.ordered_artifacts.all()]# sddb client: download every member .zarr into data/
sddb-cli download --collection geo-gse314851 --dest data/Licenses: 16 NCBI GEO policy
@article{Amrute_2026,
title = {Targeting modulated vascular smooth muscle cells in atherosclerosis via FAP-directed immunotherapy},
volume = {392},
ISSN = {1095-9203},
url = {http://dx.doi.org/10.1126/science.adx1736},
DOI = {10.1126/science.adx1736},
number = {6793},
journal = {Science},
publisher = {American Association for the Advancement of Science (AAAS)},
author = {Amrute, Junedh M. and Jung, In-Hyuk and Yamawaki, Tracy and Lin, Wen-Ling and Bredemeyer, Andrea and Diekmann, Johanna and Hayat, Sikander and Zhang, Xianglong and Wakefield, Devin L. and Luo, Xin and Maryam, Sidrah and Heo, Gyu Seong and Yang, Steven and Lee, Chang Jie Mick and Wang, Chen and Chou, Caroline and Kuppe, Christoph and Cook, Kevin D. and Kovacs, Atilla and Chintalgattu, Vishnu and Pruitt, Danielle and Barreda, Jose and Stitziel, Nathan O. and Cheng, Paul and Liu, Yongjian and Kramann, Rafael and Kreisel, Daniel and Foo, Roger S.-Y. and Rulifson, Ingrid C. and Martin, Scott and Grunert, David and Thomas, Melissa and Cui, Jixin and Quertermous, Thomas and Bengel, Frank M. and Jackson, Simon and Li, Chi-Ming and Ason, Brandon and Lavine, Kory J.},
year = {2026},
month = Apr
}